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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Ramu, Agusthiyar
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Computer Science & IT

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Bolstering image encryption techniques with blockchain technology - a systematic review Annadurai, Narmadha; Ramu, Agusthiyar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp594-604

Abstract

Multimedia data plays a momentous role in present world. With the advancements in various fields of research like internet of things (IoT), industrial IoT (IIoT), cloud computing, medical image processing, and many more technologies, the digital images have already encroached the multimedia eon. The major challenge lies in providing a tamper proof image with higher level of security and confidentiality while being transmitted through a public network. Image encryption techniques are considered to be the predominant method to anticipate security from any unauthorized user access. This has indeed provoked the researchers to create new diverse and hybrid algorithms for encrypting the images. At present blockchain has been the most prevalently discussed method for security and the next level of security can be foreseen using the blockchain encryption techniques. This paper identifies the literature which mainly focuses on assorted image encryption techniques with blockchain technology applied on digital images from heterogeneous sources. An overview has been proposed to discuss on these techniques.
NLP-based fraudulent biomedical news identification using LSTM-SGD deep learning algorithm Dhievaraj, Siva; Ramu, Agusthiyar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp179-188

Abstract

Concern over bio medical fake news is rising, particularly as false information about illnesses, medical procedures, and public health regulations becomes more prevalent. It is essential to recognize such false information, and deep learning (DL) algorithms can offer a potent remedy, especially when paired with sophisticated natural language processing (NLP) methods. This technique improves the model's capacity to ignore frequently used but uninformative terms and concentrate on important terminology. The model's capacity to concentrate on the most pertinent phrases for fake news identification is enhanced by the use of chi-squared, a statistical test that ascertains the dependency between various variables and aids in the removal of unnecessary data. By reducing less significant characteristics to zero, the Lasso approach, a kind of regression, is used for feature selection, guaranteeing that the model only utilizes the most predictive features for classification. A crucial step in getting the data ready for DL models is feature extraction, which turns unprocessed text into numerical data. After the structured data has been analyzed, algorithms like as stochastic gradient descent (SGD), long short-term memory (LSTM) may determine whether or not an article is accurate. The authenticity and dependability of medical information provided across platforms may be ensured by effectively identifying biomedical fake news by fusing DL with sophisticated NLP techniques.
Co-Authors Annadurai, Narmadha Dhievaraj, Siva